Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Gaus, Yona Falinie A"'
Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out of the training distribution remains unexplored. Whilst recent approach
Externí odkaz:
http://arxiv.org/abs/2407.15763
The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot segmentation ability. SAM operates by generating masks based on various
Externí odkaz:
http://arxiv.org/abs/2404.12285
Autor:
Corona-Figueroa, Abril, Bond-Taylor, Sam, Bhowmik, Neelanjan, Gaus, Yona Falinie A., Breckon, Toby P., Shum, Hubert P. H., Willcocks, Chris G.
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks
Externí odkaz:
http://arxiv.org/abs/2308.14152
Publikováno v:
Volume 4: VISAPP, ISBN 978-989-758-555-5, ISSN 2184-4321, pages 868-876. 2023
Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Abnormal classes are not present during training meaning that models must learn effective rep-resentations solely across normal clas
Externí odkaz:
http://arxiv.org/abs/2303.03925
Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression c
Externí odkaz:
http://arxiv.org/abs/2205.08002
The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond. Here, we explore the viability of two recent end-to-end object detection CNN archi
Externí odkaz:
http://arxiv.org/abs/2110.04906
Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb}) X-ray imagery.
Externí odkaz:
http://arxiv.org/abs/2108.12505
X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic detection and classification of weapons such as firearms and knives wit
Externí odkaz:
http://arxiv.org/abs/1911.08966
X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anom
Externí odkaz:
http://arxiv.org/abs/1911.08216
Publikováno v:
In Proc. British Machine Vision Conference Workshops, BMVA, 2019
Detecting prohibited items in X-ray security imagery is pivotal in maintaining border and transport security against a wide range of threat profiles. Convolutional Neural Networks (CNN) with the support of a significant volume of data have brought ad
Externí odkaz:
http://arxiv.org/abs/1909.11508